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Module fordead.writing_data

Created on Fri Nov 6 17:32:26 2020

@author: Raphael Dutrieux

Functions

convert_dateindex_to_datenumber

def convert_dateindex_to_datenumber(
    dataset,
    dates
)
Converts array containing dates as an index to an array containing dates as the number of days since "2015-01-01" or to a no data value if masked

Parameters
----------
dataset : xarray dataset with at least arrays : 
    "state", binary array with False where returned array will take a no data value (99999999)
    "first date", array containing index of date in 'dates'
dates : array
    Array of dates in the format "YYYY-MM-DD"

Returns
-------
results_date_number : xarray DataArray
    DataArray with dates as the number of days since "2015-01-01", or no data value of 99999999

export_csv

def export_csv(
    data_directory,
    ground_obs_path,
    ground_obs_buffer=None,
    name_column='id'
)
Produce the anomaly detection from the model, along with exporting as two .csv files data relating to pixels within the ground_obs_path shapefile.

Parameters
----------
data_directory: str
    Path of the output directory
ground_obs_path: str
    Path to the shapefile containing ground observation polygons
ground_obs_buffer: int
    Buffer applied to vectors, positive for dilation, negative for erosion.
name_column: str
    Name of the column containing the ID of the ground observation polygon

get_bins

def get_bins(
    start_date,
    end_date,
    frequency,
    dates
)
Creates bins from the start_date (or first used SENTINEL date if it is later than the start date) to the end_date (or last used SENTINEL date if it is earlier than the end_date) with specified frequency

Parameters
----------
start_date : str
    Date in the format 'YYYY-MM-DD'
end_date : str
    Date in the format 'YYYY-MM-DD'
frequency : str
    Frequency as used in pandas.date_range (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.date_range.html), e.g. 'M' (monthly), '3M' (three months), '15D' (fifteen days). It can also be 'sentinel', then bins correspond to the list given as parameter 'dates'
dates : list
    List of dates used for detection

Returns
-------
bins_as_date : numpy array
    Bins as an array of dates in the format 'YYYY-MM-DD'
bins_as_datenumber : numpy array
    Bins as an array of integers corresponding to the number of days since "2015-01-01"

get_periodic_results_as_shapefile

def get_periodic_results_as_shapefile(
    first_date_number,
    bins_as_date,
    bins_as_datenumber,
    relevant_area,
    attrs
)
Aggregates pixels in array containing dates, based on the period they fall into, then vectorizes results masking dates oustide the bins.

Parameters
----------
first_date_number : array
    Array containing dates as the number of days since "2015-01-01"
bins_as_date : array
    Array containing dates used as bins in the format "YYYY-MM-JJ"
bins_as_datenumber : array
    Array containing dates used as bins, as the number of days since "2015-01-01" 
relevant_area : array
    Mask where pixels with value False will be ignored.
attrs : dict
    Dictionnary containing 'tranform' and 'crs' to create the vector.

Returns
-------
period_end_results : geopandas dataframe
    Polygons containing the period during which dates fall.

get_state_at_date

def get_state_at_date(
    state_code,
    relevant_area,
    attrs
)
Vectorizes array 'state_code' using 'relevant_area' as mask

Parameters
----------
state_code : array
    Array to vectorize, in which value 0 is ignored, 1 is 'Anomaly', 2 is "Bare ground" and 3 is 'Bare ground after anomaly'
relevant_area : array
    Mask where pixels with value False will be ignored.
attrs : dict
    Dictionnary containing 'tranform' and 'crs' to create the vector.

Returns
-------
period_end_results : geopandas dataframe
    Polygons from vectorization of state_code, aggregated according to value 'Anomaly', "Bare ground" and 'Bare ground after anomaly'

union_confidence_class

def union_confidence_class(
    periodic_results,
    confidence_class
)
Computes union between periodic_results containing the dates of detection, and confidence_class containing the confidence classes
Polygons not intersecting correspond to areas detected as bare ground and their class column is filled with "Bare ground"

Parameters
----------
period_end_results : geopandas dataframe
    Polygons where dieback is detected, containing the period of the first anomaly detected
confidence_class : geopandas dataframe
    Polygons containing the confidence class

Returns
-------
union : geopandas dataframe
    Union of periodic_results and confidence_class

vectorizing_confidence_class

def vectorizing_confidence_class(
    confidence_index,
    nb_dates,
    relevant_area,
    bins_classes,
    classes,
    attrs
)
Classifies pixels in the relevant area into dieback classes based on the confidence index and the number of unmasked dates since the first anomaly. 

Parameters
----------
confidence_index : xarray DataArray (x,y) (float)
    Confidence index.
nb_dates : xarray DataArray (x,y) (int)
    number of unmasked dates since the first anomaly.
relevant_area : xarray DataArray (x,y) (bool)
    Array with True where pixels will be vectorized, and False where ignored.
bins_classes : list of float
    List of bins to classify pixels based on confidence_index
classes : list of str
    List with names of classes (length of classes must be length of bins_classes + 1)
attrs : dict
    Dictionnary containing 'tranform' and 'crs' to create the vector.

Returns
-------
gp_results : geopandas geodataframe
    Polygons from pixels in the relevant areacontaining the dieback class.

write_tif

def write_tif(
    data_array,
    attributes,
    path,
    nodata=None
)
Writes raster to the disk

Parameters
----------
data_array : xarray DataArray
    Object to be written
attributes : dict
    Dictionnary containing attributes used to write the data_array ("crs","nodata","scales","offsets")
path : str
    Path of the file to which data will be written
nodata : int or float, optional
    Number used as nodata. If None, the nodata attribute of the object will be kept. The default is None.

Returns
-------
None.